At a Glance
- Tasks: Lead a team to deploy scalable ML solutions and shape the architecture.
- Company: Join a forward-thinking company focused on innovative ML engineering.
- Benefits: Competitive salary, flexible hybrid work, and opportunities for professional growth.
- Other info: Dynamic team environment with a focus on collaboration and continuous improvement.
- Why this job: Make a real impact by enabling data scientists to deploy high-impact models.
- Qualifications: 5+ years in ML engineering with strong Python skills and cloud experience.
The predicted salary is between 60000 - 80000 £ per year.
We're building a new ML Engineering team and are looking for a strong technical lead to help take our machine learning capability from proof-of-concept to fully scaled, production-ready solutions.
Sitting within our Group & Enterprise Services (GES) function, this role is part of the Data vertical and reports into the Head of Data Engineering. You'll be hands-on with cloud infrastructure, APIs and deployment pipelines, working mainly in GCP Vertex AI (essential) and Azure (desirable). Your focus will be enabling data scientists to deploy high-impact models reliably and at scale.
You'll combine leadership, architectural thinking and deep engineering skills to shape the ML platform, coach engineers and deliver robust, enterprise-ready ML services.
What you'll do
- Lead, mentor and develop a small team of ML Engineers
- Oversee delivery of ML capabilities and support planning and capacity needs
- Shape architecture from early design through to production
- Build and maintain Python APIs (Flask/FastAPI) for model serving
- Develop infrastructure for real-time and batch deployments
- Design and maintain CI/CD pipelines for models
- Ensure code quality, engineering best practice and scalable cloud deployments
- Collaborate with data scientists, platform engineers and developers
- Support model lifecycle management, monitoring and automation
- Break down solution designs into deliverables and milestones
What you'll bring
- 5+ years as an ML Engineer with strong Python engineering skills
- Experience deploying and maintaining ML models in production (Vertex AI required)
- Strong software engineering fundamentals: OOP, unit testing, TDD
- Cloud experience (GCP, AWS or Azure) and IaC tools such as Terraform
- Experience with Docker, CI/CD pipelines and Git workflows
- Understanding of data science principles and taking research code to production
- Strong problem-solving skills and the ability to work independently
- Comfortable working in Agile teams
- Clear communication, collaboration and a proactive, improvement-driven mindset
ML Ops Engineer | York Hybrid in London employer: Oliver James
Join our innovative team in York, where we are dedicated to fostering a collaborative and growth-oriented work culture. As an ML Ops Engineer, you'll not only lead and mentor a talented group of engineers but also have access to cutting-edge technology and resources that empower you to make a significant impact on our machine learning capabilities. With a strong emphasis on professional development and a supportive environment, this role offers a unique opportunity to advance your career while contributing to meaningful projects.
StudySmarter Expert Advice🤫
We think this is how you could land ML Ops Engineer | York Hybrid in London
✨Get Involved in Data Science Meetups
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We think you need these skills to ace ML Ops Engineer | York Hybrid in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Oliver James, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Oliver James. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Oliver James
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Oliver James!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
